One of factors required to allow an automobile to run safely is a tire air pressure. When the air pressure is lower than an appropriate value, the stable maneuverability or fuel consumption is deteriorated, which may cause a tire burst. Thus, Tire Pressure Monitoring System (TPMS) for detecting a tire having a decreased pressure to send an alarm to the driver to prompt an appropriate action is an important technique from the viewpoint of environment protection and driver safety.
A conventional alarm apparatus can be classified into two types of the direct detection-type one (direct TPMS) and the indirect detection-type one (indirect TPMS). The direct TPMS provides a pressure sensor in a tire wheel to thereby directly measure the tire pressure. The direct TPMS can detect a decrease in the pressure at a high accuracy but requires exclusive wheels and has a problematic fault-tolerance performance in an actual environment. Thus, the direct TPMS is still disadvantageous in the technical and cost aspects.
On the other hand, the indirect TPMS is a method of estimating the air pressure based on the tire rotation information. The indirect TPMS can be further classified into the Dynamic Loaded Radius (DLR) method and the Resonance Frequency Mechanism (RFM) method. The DLR method is a method that uses a phenomenon according to which a tire having a decreased pressure in a running vehicle is collapsed and thus the tire has a reduced dynamic loaded radius and is consequently rotated at a higher speed than other tires having a normal pressure. The DLR method compares the rotation rates of the four tires to thereby detect a tire having a decreased pressure. Since this method can use only wheel rotation speed signals obtained from a wheel speed sensor to subject the signals to a relatively-easy computation processing, this method has been widely researched mainly for the purpose of detecting a puncture of one wheel. However, this method merely makes a relative comparison among wheel rotation speeds and thus cannot detect a case of four wheels simultaneous deflation (natural leakage).
Furthermore, a disadvantage is caused where a decreased pressure cannot be accurately detected through all running conditions because a difference in the wheel speed is caused also by running conditions such as the turning of the vehicle, the acceleration and deceleration, and an eccentric load.
On the other hand, the RFM method is a method to use a fact that a tire having a decreased pressure has a different wheel speed signal frequency characteristic to thereby detect a difference from a normal pressure. In contrast with the DLR method, the RFM method is an absolute comparison with the normal values of the respective wheels that are retained in advance. Thus, the RFM method also can detect a case of four wheels simultaneous deflation.
Thus, the RFM method attracts attentions as a better indirect detection method. However, the RFM method has a disadvantage where some running conditions cause strong noise for example and thus an estimated frequency value of a target domain is not robust against the vehicle speed and the road surface situation for example. The present invention relates to an apparatus for detecting a tire status based on the RFM method. Hereinafter, the basic principle of this method will be described in more detail.
When the vehicle is running, the tires receive a force from the road surface to thereby cause the torsional motion in the front-and-rear direction and the front-and-rear motion of the suspension, and these motions have a coupled resonance vibration. Since this resonance phenomenon also has an influence on the wheel rotation motion, a wheel speed signal obtained from a wheel sensor provided in the Anti-Lock Braking System (ABS) also includes information related to the resonance phenomenon. Furthermore, since the coupled resonance vibration is caused in a unique vibration mode due to the tire torsional rigidity, the excitation status thereof changes so as to depend only on a change in the air pressure constituting the tire physical characteristic and has a very small dependence on a change in the vehicle speed and a change in the road surface. Specifically, a decreased air pressure causes a change in the dynamics of the tire torsional motion. Thus, when the wheel speed signal is subjected to a frequency analysis, a peak of the coupled resonance vibration (resonance peak) appears at the lower frequency-side in the case of a decreased pressure than in the case of a normal pressure.
FIG. 3 illustrates the result of the analysis by Fast Fourier Transform (FFT) of tire vibrations when the air pressure status is a normal pressure (221 kPa), a 15%-decreased pressure from the normal pressure (188 kPa), a 25%-decreased pressure (166 kPa), and a 40%-decreased pressure (133 kPa). It can be seen that a frequency corresponding to peak values existing in the vicinity of 25 to 30 Hz (resonance frequency) moves to the lower frequency-side due to a change in the internal pressure. This phenomenon appears to be independent, due to the above-described characteristic, from the tire type and the vehicle type, the running speed, and the road surface situation for example. Thus, the RFM method focuses on this resonance frequency and issues an alarm when the frequency is relatively lower than a reference frequency estimated during initialization. Thus, the resonance frequency must be estimated based on wheel speed signals obtained from the ABS. However, since it is difficult to store time-series data in an in-vehicle calculator having a limited calculation resource, a difficulty is caused in performing the frequency analysis based on FFT. Due to this reason, the conventional method was to estimate a resonance frequency by an on-line method described below.
Since wheel speed signals are obtained as time-series data at the respective times, the data is subjected to a time-series analysis based on the K-order Autoregressive (AR) model. Specifically, parameters θ={a1, . . . , aK} in a model represented by the following formula (1) are estimated by the Kalman filter (iterative least squares technique).
                              y          ⁡                      (            t            )                          =                                            ∑                              i                =                1                            K                        ⁢                                                  ⁢                                          a                i                            ⁢                              y                ⁡                                  (                                      t                    -                    i                                    )                                                              +          ɛ                                    (        1        )            
In the formula, y (t) represents a wheel speed at the time t, ε represents white noise, and K represents the order of the model (K=2 can be established when a quadratic model is assumed in order to express a phenomenon such as vibration). A frequency corresponding to a pole of a transfer function representing an AR model is estimated as a resonance frequency. Thus, if the resonance peak can be correctly extracted based on the model, the resonance frequency can be obtained correctly.
By the way, the Tire Pressure Monitoring System must make, based on the sequence of resonance frequencies estimated at the respective times by the conventional method for example, a final determination as to whether a tire has a decreased pressure or not. Even when the resonance frequency is estimated correctly, the determination regarding the decreased pressure has two problems as described below.
First, some types of tires and vehicles show a small difference in the resonance frequency between a normal pressure and a decreased pressure (hereinafter, this difference will be called “pressure decrease sensitivity”. However, this difference intends to mean, when the term “resonance frequency” herein means not a true resonance frequency but frequencies estimated by the above system such as the AR model, a difference in the average values of the distribution thereof. Attention must be paid on the point that individual tires have different tire resonance frequencies due to a production tolerance for example even when the tires have the same brand and on the point that, regardless of the true resonance frequency, the estimate values are dispersed within an estimate dispersion range due to an influence by noise or the like). In this case, when the pressure decrease sensitivity is lower than the dispersion of the resonance frequencies, it is difficult to carry out the determination of a decreased pressure accurately. Specifically, there has been a conventionally-used method of determining a decreased pressure to issue an alarm when a difference between a reference frequency estimated during initialization and a resonance frequency estimated at the current time is larger than a difference amount set in advance. However, when standard deviation of the distribution of resonance frequencies estimated at a normal pressure is 1 Hz with regard to a tire having a pressure decrease sensitivity of 3 Hz, any set difference amount may cause a failure to issue an alarm when a low reference frequency is set due to the dispersion of running conditions. There is also a possibility of a false alarm when a high reference frequency is set. In other words, when the distribution of estimate resonance frequencies at a normal pressure is superposed on the skirt of the distribution at a decreased pressure, it is difficult to make an accurate determination in a unique manner. In such a case, a measure may be considered to reject the estimate result to pass on the determination of a decreased pressure for example. However, this is not a substantial solution and thus may cause an inconvenience depending on running conditions.
Secondly, commercially-available passenger vehicles are generally specified to have default tires of a plurality of brands. These tires have different tread patterns, inch sizes, and tire profiles or the like from one another. The different tire properties as described above cause a difference in the pressure decrease sensitivity and the resonance frequency under the respective air pressure conditions. However, with regard to vehicles attached with any of the default tires (which tire is attached to the vehicle is unknown to the system), a decreased tire pressure must be detected by a single alarm system. Therefore, in the stage of the initialization (which means a procedure given to the system for a fixed period of time after the air pressure adjustment to store a resonance frequency in the normal air pressure status), it is required to determine which default tires are attached to the vehicle and to consider the properties different depending on the tire in the stage of determining a decreased tire pressure.
When the above two problems simultaneously occur (e.g., when a plurality of default tires have highly-dispersed resonance frequencies or pressure decrease sensitivities), the determination of a decreased pressure is particularly difficult, which has been a significant disadvantage hindering the practical use of an air pressure alarm system based on the RFM method.